Failure Surface Coverage

Failure Surface Coverage treats evaluation as a map of everything that can go wrong in a deployed AI system, not a single accuracy score. The surface has layers: retrieval misses, tool-call errors, latency spikes, cost overruns, tone drift, and safety breaches. Each layer needs its own probe, and the gaps between probes are where client-facing incidents live. Agencies that map the surface before launch can scope retainers around the layers they actually cover, then charge for the ones they do not. A voice agent build illustrates the split: Cekura simulates thousands of personas and flags gibberish, interruption, and latency issues before go-live, while Hume AI layers emotion tagging and human rater feedback across 48+ emotions. Those are two different surface layers, two different line items. When a client asks why monitoring costs what it does, the answer is a coverage map, not a dashboard screenshot.